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A Class-incremental and Few-shot learning model for Intrusion detection under Concept drift

Network Intrusion Detection Systems (NIDS) based on machine learning must evolve after deployment to promptly detect emerging threats from only a few labeled samples, without catastrophically forgetting known attacks or degrading under concept drift. Existing methods address these issues in isolation, lacking a unified...